forthcoming study posted to the arXiv preprint server, researchers at the Massachusetts Institute of Technology, Stanford University, and Google explore this “apparently mysterious” phenomenon, which is called “in-context learning. ” Normally, to accomplish a new task, most machine learning models need to be retrained on new data, a process that can normally require researchers to input thousands of data points to get the output they desire—a tedious and time-consuming endeavor.  in a statement that the study is a “stepping stone to understanding how models can learn more complex tasks, and will help researchers design better training methods for language models to further improve their performance